A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors

Dorothy Koveal; Paul C. Rosen; Dylan J. Meyer; Carlos Manlio Díaz-García; Yongcheng Wang; Li-Heng Cai; Peter J. Chou; David A. Weitz; Gary Yellen

doi:10.1038/s41467-022-30685-x

Nature Communications (May 2022)

A high-throughput multiparameter screen for accelerated development and optimization of soluble genetically encoded fluorescent biosensors

Dorothy Koveal,
Paul C. Rosen,
Dylan J. Meyer,
Carlos Manlio Díaz-García,
Yongcheng Wang,
Li-Heng Cai,
Peter J. Chou,
David A. Weitz,
Gary Yellen

Affiliations

Dorothy Koveal: Department of Neurobiology, Harvard Medical School
Paul C. Rosen: Department of Neurobiology, Harvard Medical School
Dylan J. Meyer: Department of Neurobiology, Harvard Medical School
Carlos Manlio Díaz-García: Department of Neurobiology, Harvard Medical School
Yongcheng Wang: Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University
Li-Heng Cai: Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University
Peter J. Chou: Department of Neurobiology, Harvard Medical School
David A. Weitz: Department of Physics and John A. Paulson School of Engineering and Applied Sciences, Harvard University
Gary Yellen: Department of Neurobiology, Harvard Medical School

DOI: https://doi.org/10.1038/s41467-022-30685-x
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 14

Abstract

Read online

Fluorescent biosensors are important tools for studying cellular metabolism, but development and optimization are challenging. Koveal et al. present a high-throughput multiparameter screen for sensor performance, and used it to generate LiLac, a high-performance, quantitative lactate sensor.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

About the journal